In the intelligent inspection of furniture boards, wood debris generated during manufacturing can interfere with the imaging process. This leads to burr disturbances at the corners of the boards. Existing corner detection methods exhibit lower detection accuracy under the interference of these disturbances. To overcome this issue, this paper presents a corner detection algorithm tailored for furniture boards that incorporates the Random Sample Consensus (RANSAC)algorithm and line fitting techniques based on the Huber loss function. To enhance detection efficiency, our algorithm initially identifies horizontal and vertical edges near a corner. This preliminary step facilitates subsequent corner detection. This study introduces a test dataset for evaluating the accuracy and efficiency of algorithms. A battery of comparative experiments, including benchmarks with conventional methods, were conducted. The results demonstrate that our algorithm significantly enhances the efficiency and robustness of corner detection under a variety of complex burr interference conditions.
Filter pruning is a widely employed model compression technique, with the inter-channel method currently recognized as the most efficient approach for filter pruning. However, existing inter-channel methods have not fully explored the independence between convolutional channels. In this paper, we propose to use the Schatten p-norm to extract rank information between convolutional channels and measure the importance of a specific channel by analyzing the change in rank information after its removal. The principle underlying our pruning approach is that a smaller change in rank information corresponds to a lesser degree of importance for the channel. Besides, to reduce the computation time required for calculating channel importance, we propose employing a prototype-based approach. We have verified the effectiveness and efficiency of our proposed method on various datasets and models. As an example, when applying our approach to ResNet-56, we achieved an accuracy improvement of 0.91% while the model size and FLOPs were reduced by 42.8% and 47.4% respectively on the CIFAR10 dataset.
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